import gradio as gr import os from langchain.document_loaders import PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import Chroma from langchain.chains import ConversationalRetrievalChain from langchain.embeddings import HuggingFaceEmbeddings from langchain.llms import HuggingFacePipeline from langchain.chains import ConversationChain from langchain.memory import ConversationBufferMemory from langchain.llms import HuggingFaceHub from transformers import AutoTokenizer import transformers import torch import tqdm import accelerate # default_persist_directory = './chroma_HF/' llm_name1 = "mistralai/Mistral-7B-Instruct-v0.2" llm_name2 = "mistralai/Mistral-7B-Instruct-v0.1" llm_name3 = "meta-llama/Llama-2-7b-chat-hf" llm_name4 = "microsoft/phi-2" llm_name5 = "mosaicml/mpt-7b-instruct" llm_name6 = "tiiuae/falcon-7b-instruct" llm_name7 = "google/flan-t5-xxl" list_llm = [llm_name1, llm_name2, llm_name3, llm_name4, llm_name5, llm_name6, llm_name7] list_llm_simple = [os.path.basename(llm) for llm in list_llm] # Load PDF document and create doc splits def load_doc(list_file_path, chunk_size, chunk_overlap): # Processing for one document only # loader = PyPDFLoader(file_path) # pages = loader.load() loaders = [PyPDFLoader(x) for x in list_file_path] pages = [] for loader in loaders: pages.extend(loader.load()) # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50) text_splitter = RecursiveCharacterTextSplitter( chunk_size = chunk_size, chunk_overlap = chunk_overlap) doc_splits = text_splitter.split_documents(pages) return doc_splits # Create vector database def create_db(splits): embedding = HuggingFaceEmbeddings() vectordb = Chroma.from_documents( documents=splits, embedding=embedding, # persist_directory=default_persist_directory ) return vectordb # Load vector database def load_db(): embedding = HuggingFaceEmbeddings() vectordb = Chroma( persist_directory=default_persist_directory, embedding_function=embedding) return vectordb # Initialize langchain LLM chain def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): progress(0.1, desc="Initializing HF tokenizer...") # HuggingFacePipeline uses local model # Note: it will download model locally... # tokenizer=AutoTokenizer.from_pretrained(llm_model) # progress(0.5, desc="Initializing HF pipeline...") # pipeline=transformers.pipeline( # "text-generation", # model=llm_model, # tokenizer=tokenizer, # torch_dtype=torch.bfloat16, # trust_remote_code=True, # device_map="auto", # # max_length=1024, # max_new_tokens=max_tokens, # do_sample=True, # top_k=top_k, # num_return_sequences=1, # eos_token_id=tokenizer.eos_token_id # ) # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature}) # HuggingFaceHub uses HF inference endpoints progress(0.5, desc="Initializing HF Hub...") # Use of trust_remote_code as model_kwargs # Warning: langchain issue # URL: https://github.com/langchain-ai/langchain/issues/6080 if llm_model == "microsoft/phi-2": llm = HuggingFaceHub( repo_id=llm_model, model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"} ) else: llm = HuggingFaceHub( repo_id=llm_model, # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"} model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k} ) progress(0.75, desc="Defining buffer memory...") memory = ConversationBufferMemory( memory_key="chat_history", output_key='answer', return_messages=True ) # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3}) retriever=vector_db.as_retriever() progress(0.8, desc="Defining retrieval chain...") qa_chain = ConversationalRetrievalChain.from_llm( llm, retriever=retriever, chain_type="stuff", memory=memory, # combine_docs_chain_kwargs={"prompt": your_prompt}) return_source_documents=True, # return_generated_question=True, # verbose=True, ) progress(0.9, desc="Done!") return qa_chain # Initialize database def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()): # Create list of documents (when valid) #file_path = file_obj.name list_file_path = [x.name for x in list_file_obj if x is not None] # print('list_file_path', list_file_path) progress(0.25, desc="Loading document...") # Load document and create splits doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap) # Create or load Vector database progress(0.5, desc="Generating vector database...") # global vector_db vector_db = create_db(doc_splits) progress(0.9, desc="Done!") return vector_db, "Complete!" def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): # print("llm_option",llm_option) llm_name = list_llm[llm_option] print("llm_name: ",llm_name) qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress) return qa_chain, "Complete!" def format_chat_history(message, chat_history): formatted_chat_history = [] for user_message, bot_message in chat_history: formatted_chat_history.append(f"User: {user_message}") formatted_chat_history.append(f"Assistant: {bot_message}") return formatted_chat_history def conversation(qa_chain, message, history): formatted_chat_history = format_chat_history(message, history) #print("formatted_chat_history",formatted_chat_history) # Generate response using QA chain response = qa_chain({"question": message, "chat_history": formatted_chat_history}) response_answer = response["answer"] response_sources = response["source_documents"] response_source1 = response_sources[0].page_content.strip() response_source2 = response_sources[1].page_content.strip() # Langchain sources are zero-based response_source1_page = response_sources[0].metadata["page"] + 1 response_source2_page = response_sources[1].metadata["page"] + 1 # print ('chat response: ', response_answer) # print('DB source', response_sources) # Append user message and response to chat history new_history = history + [(message, response_answer)] # return gr.update(value=""), new_history, response_sources[0], response_sources[1] return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page def upload_file(file_obj): list_file_path = [] for idx, file in enumerate(file_obj): file_path = file_obj.name list_file_path.append(file_path) # print(file_path) # initialize_database(file_path, progress) return list_file_path def demo(): with gr.Blocks(theme="base") as demo: vector_db = gr.State() qa_chain = gr.State() gr.Markdown( """

PDF-based chatbot (powered by LangChain and open-source LLMs)

Ask any questions about your PDF documents, along with follow-ups

Note: This AI assistant performs retrieval-augmented generation from your PDF documents. \ When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.
Warning: This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate an output.
""") with gr.Tab("Step 1 - Document pre-processing"): with gr.Row(): document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)") # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1) with gr.Row(): db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database") with gr.Accordion("Advanced options - Document text splitter", open=False): with gr.Row(): slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True) with gr.Row(): slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True) with gr.Row(): db_progress = gr.Textbox(label="Vector database initialization", value="None") with gr.Row(): db_btn = gr.Button("Generate vector database...") with gr.Tab("Step 2 - QA chain initialization"): with gr.Row(): llm_btn = gr.Radio(list_llm_simple, \ label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model") with gr.Accordion("Advanced options - LLM model", open=False): with gr.Row(): slider_temperature = gr.Slider(minimum = 0.0, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True) with gr.Row(): slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True) with gr.Row(): slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True) with gr.Row(): llm_progress = gr.Textbox(value="None",label="QA chain initialization") with gr.Row(): qachain_btn = gr.Button("Initialize question-answering chain...") with gr.Tab("Step 3 - Conversation with chatbot"): chatbot = gr.Chatbot(height=300) with gr.Accordion("Advanced - Document references", open=False): with gr.Row(): doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20) source1_page = gr.Number(label="Page", scale=1) with gr.Row(): doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20) source2_page = gr.Number(label="Page", scale=1) with gr.Row(): msg = gr.Textbox(placeholder="Type message", container=True) with gr.Row(): submit_btn = gr.Button("Submit") clear_btn = gr.ClearButton([msg, chatbot]) # Preprocessing events #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document]) db_btn.click(initialize_database, \ inputs=[document, slider_chunk_size, slider_chunk_overlap], \ outputs=[vector_db, db_progress]) qachain_btn.click(initialize_LLM, \ inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \ outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0], \ inputs=None, \ outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], \ queue=False) # Chatbot events msg.submit(conversation, \ inputs=[qa_chain, msg, chatbot], \ outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page], \ queue=False) submit_btn.click(conversation, \ inputs=[qa_chain, msg, chatbot], \ outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page], \ queue=False) clear_btn.click(lambda:[None,"",0,"",0], \ inputs=None, \ outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], \ queue=False) demo.queue().launch(debug=True) if __name__ == "__main__": demo()